The present invention relates to techniques that perform operations plural data items.
Many conventional techniques perform operations on plural data items. Some examples arise in the field of image processing, in which each data item can relate to a pixel in an image.
Mahoney, EP-A 460 970, describes hierarchical data analysis techniques that can be applied to pixel values. FIG. 7 shows a Connection Machine system that performs image processing by simulating a binary image jungle (BIJ). A front end processor can make calls to cause processing units in the Connection Machine to perform arithmetic and logical operations. FIG. 8 illustrates a part of an array of processing units in a Connection Machine, with a processing unit storing a pixel value. FIG. 15 illustrates how binary attributes, fullness and vacantness, can be encoded using a number indicating a level at which a transition takes place. Page 22 lines 18-19 mention that a downward attribute value could be produced by applying a criterion. FIG. 17 illustrates a technique in which an array is produced by comparing each value in an array with neighboring values. As described in relation to FIGS. 18 and 19, chunks of an image that meet a validity criterion can be found by producing an exhaustive hierarcy of data items. As described in relation to FIG. 22, a saliency criterion, such as maximum or minimum attribute value, can be used to decide from which component a pixel should take a label. Selection criteria are described at page 28 and illustrated in FIGS. 24 and 25. Page 31, beginning at line 15, describes extensions to handle color and gray shaded images.
Bloomberg et al., EP-A 431 961, describe image reduction and enlargement techniques. Page 2 line 18-page 3 line 18 and page 5 line 50-page 6 line 31 describe methods of reducing a binary image by performing logical operations between bits. Page 3 lines 36-58 describe techniques for operating on very wide pixelwords that include shifting a pixelword and then logically combining the pixelword with a shifted version of itself. The result is then compressed. Page 4 lines 43 and 44 define AND, OR, and XOR as logical operations carried out between two images on a pixel-by-pixel basis, while NOT is a logical operation carried out on a single image on a pixel-by-pixel basis.
As Bloomberg et al. note in relation to FIG. 1, a scanner may provide a gray scale image with a plurality of bits per pixel. Page 12 line 46-page 13 line 11 describe an extension in which a grayscale image is reduced to obtain a reduced grayscale image with the same number of bits/pixel by replacing the four pixel values in each 2.times.2 square by a single pixel with an approximate average value.
Bloomberg et al. note at page 14 lines 35-38 that their algorithms lend themselves to parallelism, with many processors independently performing thresholded reduction on parts of an image, such as bands of a given number of scanlines.